Overview

Brought to you by YData

Dataset statistics

Number of variables29
Number of observations500
Missing cells30
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory624.7 KiB
Average record size in memory1.2 KiB

Variable types

Text4
Categorical19
Numeric4
DateTime2

Alerts

feature_1 is highly overall correlated with product_category and 2 other fieldsHigh correlation
product_category is highly overall correlated with feature_1 and 2 other fieldsHigh correlation
sub_type is highly overall correlated with feature_1 and 2 other fieldsHigh correlation
technology is highly overall correlated with feature_1 and 2 other fieldsHigh correlation
warranty_duration_months is highly overall correlated with warranty_yearsHigh correlation
warranty_years is highly overall correlated with warranty_duration_monthsHigh correlation
capacity_tons is highly imbalanced (68.2%) Imbalance
capacity_kg is highly imbalanced (68.3%) Imbalance
capacity_place_settings is highly imbalanced (65.4%) Imbalance
manufacturing_date has 15 (3.0%) missing values Missing
review_date has 15 (3.0%) missing values Missing
discount_offered has 63 (12.6%) zeros Zeros

Reproduction

Analysis started2025-08-19 01:09:05.407184
Analysis finished2025-08-19 01:09:10.940585
Duration5.53 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct484
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size32.4 KiB
2025-08-19T01:09:11.229159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters4500
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique483 ?
Unique (%)96.6%

Sample

1st rowPID433097
2nd rowPID251899
3rd rowPID857398
4th rowPID934528
5th rowPID952837
ValueCountFrequency (%)
pid514014 17
 
3.4%
pid375281 1
 
0.2%
pid792350 1
 
0.2%
pid853697 1
 
0.2%
pid587371 1
 
0.2%
pid857398 1
 
0.2%
pid651778 1
 
0.2%
pid796565 1
 
0.2%
pid623558 1
 
0.2%
pid662738 1
 
0.2%
Other values (474) 474
94.8%
2025-08-19T01:09:11.625518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 500
11.1%
I 500
11.1%
D 500
11.1%
4 330
 
7.3%
3 327
 
7.3%
1 311
 
6.9%
2 309
 
6.9%
5 301
 
6.7%
6 299
 
6.6%
8 295
 
6.6%
Other values (3) 828
18.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 500
11.1%
I 500
11.1%
D 500
11.1%
4 330
 
7.3%
3 327
 
7.3%
1 311
 
6.9%
2 309
 
6.9%
5 301
 
6.7%
6 299
 
6.6%
8 295
 
6.6%
Other values (3) 828
18.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 500
11.1%
I 500
11.1%
D 500
11.1%
4 330
 
7.3%
3 327
 
7.3%
1 311
 
6.9%
2 309
 
6.9%
5 301
 
6.7%
6 299
 
6.6%
8 295
 
6.6%
Other values (3) 828
18.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 500
11.1%
I 500
11.1%
D 500
11.1%
4 330
 
7.3%
3 327
 
7.3%
1 311
 
6.9%
2 309
 
6.9%
5 301
 
6.7%
6 299
 
6.6%
8 295
 
6.6%
Other values (3) 828
18.4%

product_category
Categorical

High correlation 

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size34.2 KiB
Refrigerator
103 
Dishwasher
83 
Water Dispenser
82 
Washing Machine
78 
Air Cooler
78 

Length

Max length15
Median length12
Mean length12.772
Min length10

Characters and Unicode

Total characters6386
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDishwasher
2nd rowWashing Machine
3rd rowAir Conditioner
4th rowAir Conditioner
5th rowAir Conditioner

Common Values

ValueCountFrequency (%)
Refrigerator 103
20.6%
Dishwasher 83
16.6%
Water Dispenser 82
16.4%
Washing Machine 78
15.6%
Air Cooler 78
15.6%
Air Conditioner 76
15.2%

Length

2025-08-19T01:09:11.748089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:09:11.834571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
air 154
18.9%
refrigerator 103
12.7%
dishwasher 83
10.2%
dispenser 82
10.1%
water 82
10.1%
washing 78
9.6%
machine 78
9.6%
cooler 78
9.6%
conditioner 76
9.3%

Most occurring characters

ValueCountFrequency (%)
r 864
13.5%
e 767
12.0%
i 730
11.4%
a 424
 
6.6%
o 411
 
6.4%
s 408
 
6.4%
n 390
 
6.1%
h 322
 
5.0%
314
 
4.9%
t 261
 
4.1%
Other values (13) 1495
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6386
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 864
13.5%
e 767
12.0%
i 730
11.4%
a 424
 
6.6%
o 411
 
6.4%
s 408
 
6.4%
n 390
 
6.1%
h 322
 
5.0%
314
 
4.9%
t 261
 
4.1%
Other values (13) 1495
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6386
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 864
13.5%
e 767
12.0%
i 730
11.4%
a 424
 
6.6%
o 411
 
6.4%
s 408
 
6.4%
n 390
 
6.1%
h 322
 
5.0%
314
 
4.9%
t 261
 
4.1%
Other values (13) 1495
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6386
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 864
13.5%
e 767
12.0%
i 730
11.4%
a 424
 
6.6%
o 411
 
6.4%
s 408
 
6.4%
n 390
 
6.1%
h 322
 
5.0%
314
 
4.9%
t 261
 
4.1%
Other values (13) 1495
23.4%

sub_type
Categorical

High correlation 

Distinct15
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size32.8 KiB
Bottom Loading
61 
Top Load
44 
Full Size
42 
Table Top
39 
Top Loading
38 
Other values (10)
276 

Length

Max length14
Median length11
Mean length9.834
Min length5

Characters and Unicode

Total characters4917
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTable Top
2nd rowTop Load
3rd rowInverter AC
4th rowWindow AC
5th rowSplit AC

Common Values

ValueCountFrequency (%)
Bottom Loading 61
12.2%
Top Load 44
 
8.8%
Full Size 42
 
8.4%
Table Top 39
 
7.8%
Top Loading 38
 
7.6%
Front Load 33
 
6.6%
Desert 31
 
6.2%
Personal 31
 
6.2%
Side-by-Side 30
 
6.0%
Double Door 30
 
6.0%
Other values (5) 121
24.2%

Length

2025-08-19T01:09:11.963920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
top 121
13.6%
loading 99
11.1%
load 77
 
8.6%
ac 76
 
8.5%
bottom 61
 
6.8%
door 58
 
6.5%
full 42
 
4.7%
size 42
 
4.7%
table 39
 
4.4%
front 33
 
3.7%
Other values (9) 243
27.3%

Most occurring characters

ValueCountFrequency (%)
o 671
 
13.6%
391
 
8.0%
e 365
 
7.4%
i 277
 
5.6%
d 261
 
5.3%
a 246
 
5.0%
n 244
 
5.0%
t 237
 
4.8%
l 235
 
4.8%
r 226
 
4.6%
Other values (22) 1764
35.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4917
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 671
 
13.6%
391
 
8.0%
e 365
 
7.4%
i 277
 
5.6%
d 261
 
5.3%
a 246
 
5.0%
n 244
 
5.0%
t 237
 
4.8%
l 235
 
4.8%
r 226
 
4.6%
Other values (22) 1764
35.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4917
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 671
 
13.6%
391
 
8.0%
e 365
 
7.4%
i 277
 
5.6%
d 261
 
5.3%
a 246
 
5.0%
n 244
 
5.0%
t 237
 
4.8%
l 235
 
4.8%
r 226
 
4.6%
Other values (22) 1764
35.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4917
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 671
 
13.6%
391
 
8.0%
e 365
 
7.4%
i 277
 
5.6%
d 261
 
5.3%
a 246
 
5.0%
n 244
 
5.0%
t 237
 
4.8%
l 235
 
4.8%
r 226
 
4.6%
Other values (22) 1764
35.9%
Distinct485
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size32.7 KiB
2025-08-19T01:09:12.234868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.726
Min length8

Characters and Unicode

Total characters4863
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique484 ?
Unique (%)96.8%

Sample

1st rowModel_N299
2nd rowModel_K244
3rd rowModel_X75
4th rowModel_X101
5th rowModel_Y128
ValueCountFrequency (%)
model_a0 16
 
3.2%
model_t305 1
 
0.2%
model_s304 1
 
0.2%
model_f343 1
 
0.2%
model_z389 1
 
0.2%
model_u332 1
 
0.2%
model_t175 1
 
0.2%
model_y128 1
 
0.2%
model_m272 1
 
0.2%
model_g344 1
 
0.2%
Other values (475) 475
95.0%
2025-08-19T01:09:12.620779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 519
10.7%
o 500
10.3%
d 500
10.3%
e 500
10.3%
l 500
10.3%
_ 500
10.3%
1 198
 
4.1%
2 195
 
4.0%
3 191
 
3.9%
4 190
 
3.9%
Other values (31) 1070
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4863
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 519
10.7%
o 500
10.3%
d 500
10.3%
e 500
10.3%
l 500
10.3%
_ 500
10.3%
1 198
 
4.1%
2 195
 
4.0%
3 191
 
3.9%
4 190
 
3.9%
Other values (31) 1070
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4863
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 519
10.7%
o 500
10.3%
d 500
10.3%
e 500
10.3%
l 500
10.3%
_ 500
10.3%
1 198
 
4.1%
2 195
 
4.0%
3 191
 
3.9%
4 190
 
3.9%
Other values (31) 1070
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4863
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 519
10.7%
o 500
10.3%
d 500
10.3%
e 500
10.3%
l 500
10.3%
_ 500
10.3%
1 198
 
4.1%
2 195
 
4.0%
3 191
 
3.9%
4 190
 
3.9%
Other values (31) 1070
22.0%

capacity_tons
Categorical

Imbalance 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
1.5
449 
2.0
 
18
1.2
 
17
1.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1500
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.5
2nd row1.5
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.5 449
89.8%
2.0 18
 
3.6%
1.2 17
 
3.4%
1.0 16
 
3.2%

Length

2025-08-19T01:09:13.168370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:09:13.260085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.5 449
89.8%
2.0 18
 
3.6%
1.2 17
 
3.4%
1.0 16
 
3.2%

Most occurring characters

ValueCountFrequency (%)
. 500
33.3%
1 482
32.1%
5 449
29.9%
2 35
 
2.3%
0 34
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 500
33.3%
1 482
32.1%
5 449
29.9%
2 35
 
2.3%
0 34
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 500
33.3%
1 482
32.1%
5 449
29.9%
2 35
 
2.3%
0 34
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 500
33.3%
1 482
32.1%
5 449
29.9%
2 35
 
2.3%
0 34
 
2.3%

capacity_liters
Real number (ℝ)

Distinct10
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.2
Minimum15
Maximum550
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-08-19T01:09:13.339273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile15
Q150
median50
Q350
95-th percentile450
Maximum550
Range535
Interquartile range (IQR)0

Descriptive statistics

Standard deviation138.5932
Coefficient of variation (CV)1.3560979
Kurtosis3.8094691
Mean102.2
Median Absolute Deviation (MAD)0
Skewness2.2355813
Sum51100
Variance19208.076
MonotonicityNot monotonic
2025-08-19T01:09:13.435128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
50 272
54.4%
20 61
 
12.2%
15 46
 
9.2%
250 24
 
4.8%
550 23
 
4.6%
450 17
 
3.4%
90 17
 
3.4%
70 15
 
3.0%
350 13
 
2.6%
180 12
 
2.4%
ValueCountFrequency (%)
15 46
 
9.2%
20 61
 
12.2%
50 272
54.4%
70 15
 
3.0%
90 17
 
3.4%
180 12
 
2.4%
250 24
 
4.8%
350 13
 
2.6%
450 17
 
3.4%
550 23
 
4.6%
ValueCountFrequency (%)
550 23
 
4.6%
450 17
 
3.4%
350 13
 
2.6%
250 24
 
4.8%
180 12
 
2.4%
90 17
 
3.4%
70 15
 
3.0%
50 272
54.4%
20 61
 
12.2%
15 46
 
9.2%

capacity_kg
Categorical

Imbalance 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
7.0
443 
6.5
 
19
8.0
 
16
9.0
 
12
6.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1500
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7.0
2nd row6.5
3rd row7.0
4th row7.0
5th row7.0

Common Values

ValueCountFrequency (%)
7.0 443
88.6%
6.5 19
 
3.8%
8.0 16
 
3.2%
9.0 12
 
2.4%
6.0 10
 
2.0%

Length

2025-08-19T01:09:13.537994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:09:13.614062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
7.0 443
88.6%
6.5 19
 
3.8%
8.0 16
 
3.2%
9.0 12
 
2.4%
6.0 10
 
2.0%

Most occurring characters

ValueCountFrequency (%)
. 500
33.3%
0 481
32.1%
7 443
29.5%
6 29
 
1.9%
5 19
 
1.3%
8 16
 
1.1%
9 12
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 500
33.3%
0 481
32.1%
7 443
29.5%
6 29
 
1.9%
5 19
 
1.3%
8 16
 
1.1%
9 12
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 500
33.3%
0 481
32.1%
7 443
29.5%
6 29
 
1.9%
5 19
 
1.3%
8 16
 
1.1%
9 12
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 500
33.3%
0 481
32.1%
7 443
29.5%
6 29
 
1.9%
5 19
 
1.3%
8 16
 
1.1%
9 12
 
0.8%

capacity_place_settings
Categorical

Imbalance 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
12.0
443 
14.0
 
22
8.0
 
19
15.0
 
16

Length

Max length4
Median length4
Mean length3.962
Min length3

Characters and Unicode

Total characters1981
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12.0
2nd row12.0
3rd row12.0
4th row12.0
5th row12.0

Common Values

ValueCountFrequency (%)
12.0 443
88.6%
14.0 22
 
4.4%
8.0 19
 
3.8%
15.0 16
 
3.2%

Length

2025-08-19T01:09:13.710841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:09:13.785675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
12.0 443
88.6%
14.0 22
 
4.4%
8.0 19
 
3.8%
15.0 16
 
3.2%

Most occurring characters

ValueCountFrequency (%)
. 500
25.2%
0 500
25.2%
1 481
24.3%
2 443
22.4%
4 22
 
1.1%
8 19
 
1.0%
5 16
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1981
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 500
25.2%
0 500
25.2%
1 481
24.3%
2 443
22.4%
4 22
 
1.1%
8 19
 
1.0%
5 16
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1981
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 500
25.2%
0 500
25.2%
1 481
24.3%
2 443
22.4%
4 22
 
1.1%
8 19
 
1.0%
5 16
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1981
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 500
25.2%
0 500
25.2%
1 481
24.3%
2 443
22.4%
4 22
 
1.1%
8 19
 
1.0%
5 16
 
0.8%

technology
Categorical

High correlation 

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size35.4 KiB
Compressor Cooling
101 
Electronic Control
82 
Evaporative Cooling
77 
Direct Cool
50 
Non-Inverter
47 
Other values (4)
143 

Length

Max length19
Median length18
Mean length15.168
Min length8

Characters and Unicode

Total characters7584
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowElectronic Control
2nd rowFully-Automatic
3rd rowInverter
4th rowNon-Inverter
5th rowNon-Inverter

Common Values

ValueCountFrequency (%)
Compressor Cooling 101
20.2%
Electronic Control 82
16.4%
Evaporative Cooling 77
15.4%
Direct Cool 50
10.0%
Non-Inverter 47
9.4%
Frost Free 40
 
8.0%
Fully-Automatic 39
 
7.8%
Semi-Automatic 36
 
7.2%
Inverter 28
 
5.6%

Length

2025-08-19T01:09:13.883700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:09:13.983192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
cooling 178
20.9%
compressor 101
11.9%
electronic 82
9.6%
control 82
9.6%
evaporative 77
9.1%
direct 50
 
5.9%
cool 50
 
5.9%
non-inverter 47
 
5.5%
frost 40
 
4.7%
free 40
 
4.7%
Other values (3) 103
12.1%

Most occurring characters

ValueCountFrequency (%)
o 1143
15.1%
r 723
 
9.5%
e 576
 
7.6%
t 556
 
7.3%
i 498
 
6.6%
l 470
 
6.2%
n 464
 
6.1%
C 411
 
5.4%
350
 
4.6%
c 289
 
3.8%
Other values (16) 2104
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1143
15.1%
r 723
 
9.5%
e 576
 
7.6%
t 556
 
7.3%
i 498
 
6.6%
l 470
 
6.2%
n 464
 
6.1%
C 411
 
5.4%
350
 
4.6%
c 289
 
3.8%
Other values (16) 2104
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1143
15.1%
r 723
 
9.5%
e 576
 
7.6%
t 556
 
7.3%
i 498
 
6.6%
l 470
 
6.2%
n 464
 
6.1%
C 411
 
5.4%
350
 
4.6%
c 289
 
3.8%
Other values (16) 2104
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1143
15.1%
r 723
 
9.5%
e 576
 
7.6%
t 556
 
7.3%
i 498
 
6.6%
l 470
 
6.2%
n 464
 
6.1%
C 411
 
5.4%
350
 
4.6%
c 289
 
3.8%
Other values (16) 2104
27.7%

feature_1
Categorical

High correlation 

Distinct17
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size35.4 KiB
ProSmart Inverter Motor
77 
Hot & Cold
33 
StoreFresh+
33 
Hygiene+
30 
Hot, Normal & Cold
30 
Other values (12)
297 

Length

Max length23
Median length18
Mean length15.24
Min length8

Characters and Unicode

Total characters7620
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProSmart Inverter Motor
2nd rowProSmart Inverter Motor
3rd rowTurbo Mode
4th rowSelf Diagnosis
5th rowTurbo Mode

Common Values

ValueCountFrequency (%)
ProSmart Inverter Motor 77
15.4%
Hot & Cold 33
 
6.6%
StoreFresh+ 33
 
6.6%
Hygiene+ 30
 
6.0%
Hot, Normal & Cold 30
 
6.0%
NeoFrost Dual Cooling 29
 
5.8%
Turbo Mode 29
 
5.8%
GentleWave Drum 28
 
5.6%
Remote Control 28
 
5.6%
Active Fresh Blue Light 27
 
5.4%
Other values (7) 156
31.2%

Length

2025-08-19T01:09:14.131794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
87
 
7.4%
cold 87
 
7.4%
prosmart 77
 
6.5%
motor 77
 
6.5%
inverter 77
 
6.5%
hot 63
 
5.3%
normal 54
 
4.6%
cooling 52
 
4.4%
storefresh 33
 
2.8%
hygiene 30
 
2.5%
Other values (21) 541
45.9%

Most occurring characters

ValueCountFrequency (%)
o 846
 
11.1%
e 737
 
9.7%
678
 
8.9%
r 670
 
8.8%
t 553
 
7.3%
l 352
 
4.6%
a 334
 
4.4%
n 308
 
4.0%
m 250
 
3.3%
s 221
 
2.9%
Other values (31) 2671
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 846
 
11.1%
e 737
 
9.7%
678
 
8.9%
r 670
 
8.8%
t 553
 
7.3%
l 352
 
4.6%
a 334
 
4.4%
n 308
 
4.0%
m 250
 
3.3%
s 221
 
2.9%
Other values (31) 2671
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 846
 
11.1%
e 737
 
9.7%
678
 
8.9%
r 670
 
8.8%
t 553
 
7.3%
l 352
 
4.6%
a 334
 
4.4%
n 308
 
4.0%
m 250
 
3.3%
s 221
 
2.9%
Other values (31) 2671
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 846
 
11.1%
e 737
 
9.7%
678
 
8.9%
r 670
 
8.8%
t 553
 
7.3%
l 352
 
4.6%
a 334
 
4.4%
n 308
 
4.0%
m 250
 
3.3%
s 221
 
2.9%
Other values (31) 2671
35.1%
Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
3.0
178 
5.0
87 
2.0
81 
4.0
79 
1.0
75 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1500
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row1.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 178
35.6%
5.0 87
17.4%
2.0 81
16.2%
4.0 79
15.8%
1.0 75
15.0%

Length

2025-08-19T01:09:14.250836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:09:14.339552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.0 178
35.6%
5.0 87
17.4%
2.0 81
16.2%
4.0 79
15.8%
1.0 75
15.0%

Most occurring characters

ValueCountFrequency (%)
. 500
33.3%
0 500
33.3%
3 178
 
11.9%
5 87
 
5.8%
2 81
 
5.4%
4 79
 
5.3%
1 75
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 500
33.3%
0 500
33.3%
3 178
 
11.9%
5 87
 
5.8%
2 81
 
5.4%
4 79
 
5.3%
1 75
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 500
33.3%
0 500
33.3%
3 178
 
11.9%
5 87
 
5.8%
2 81
 
5.4%
4 79
 
5.3%
1 75
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 500
33.3%
0 500
33.3%
3 178
 
11.9%
5 87
 
5.8%
2 81
 
5.4%
4 79
 
5.3%
1 75
 
5.0%

color
Categorical

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size30.3 KiB
Grey
120 
Silver
102 
White
97 
Blue
95 
Black
86 

Length

Max length6
Median length5
Mean length4.774
Min length4

Characters and Unicode

Total characters2387
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlue
2nd rowSilver
3rd rowBlue
4th rowGrey
5th rowBlack

Common Values

ValueCountFrequency (%)
Grey 120
24.0%
Silver 102
20.4%
White 97
19.4%
Blue 95
19.0%
Black 86
17.2%

Length

2025-08-19T01:09:14.455287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:09:14.543460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
grey 120
24.0%
silver 102
20.4%
white 97
19.4%
blue 95
19.0%
black 86
17.2%

Most occurring characters

ValueCountFrequency (%)
e 414
17.3%
l 283
11.9%
r 222
9.3%
i 199
 
8.3%
B 181
 
7.6%
y 120
 
5.0%
G 120
 
5.0%
S 102
 
4.3%
v 102
 
4.3%
W 97
 
4.1%
Other values (6) 547
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2387
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 414
17.3%
l 283
11.9%
r 222
9.3%
i 199
 
8.3%
B 181
 
7.6%
y 120
 
5.0%
G 120
 
5.0%
S 102
 
4.3%
v 102
 
4.3%
W 97
 
4.1%
Other values (6) 547
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2387
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 414
17.3%
l 283
11.9%
r 222
9.3%
i 199
 
8.3%
B 181
 
7.6%
y 120
 
5.0%
G 120
 
5.0%
S 102
 
4.3%
v 102
 
4.3%
W 97
 
4.1%
Other values (6) 547
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2387
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 414
17.3%
l 283
11.9%
r 222
9.3%
i 199
 
8.3%
B 181
 
7.6%
y 120
 
5.0%
G 120
 
5.0%
S 102
 
4.3%
v 102
 
4.3%
W 97
 
4.1%
Other values (6) 547
22.9%

price_inr
Real number (ℝ)

Distinct484
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46561.774
Minimum8351
Maximum84989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-08-19T01:09:14.664830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8351
5-th percentile10967.8
Q126099
median46386
Q367391.5
95-th percentile81797.3
Maximum84989
Range76638
Interquartile range (IQR)41292.5

Descriptive statistics

Standard deviation22882.525
Coefficient of variation (CV)0.49144445
Kurtosis-1.2667697
Mean46561.774
Median Absolute Deviation (MAD)20459.5
Skewness-0.0049721407
Sum23280887
Variance5.2360996 × 108
MonotonicityNot monotonic
2025-08-19T01:09:14.796633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46386 16
 
3.2%
16595 2
 
0.4%
84989 1
 
0.2%
70115 1
 
0.2%
61826 1
 
0.2%
18381 1
 
0.2%
65780 1
 
0.2%
58709 1
 
0.2%
20069 1
 
0.2%
34240 1
 
0.2%
Other values (474) 474
94.8%
ValueCountFrequency (%)
8351 1
0.2%
8437 1
0.2%
8906 1
0.2%
9064 1
0.2%
9148 1
0.2%
9247 1
0.2%
9258 1
0.2%
9309 1
0.2%
9373 1
0.2%
9694 1
0.2%
ValueCountFrequency (%)
84989 1
0.2%
84931 1
0.2%
84786 1
0.2%
84533 1
0.2%
84352 1
0.2%
84281 1
0.2%
84196 1
0.2%
83970 1
0.2%
83862 1
0.2%
83766 1
0.2%

manufacturing_date
Date

Missing 

Distinct377
Distinct (%)77.7%
Missing15
Missing (%)3.0%
Memory size4.0 KiB
Minimum2022-01-03 00:00:00
Maximum2024-06-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-19T01:09:14.937698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:09:15.090326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

warranty_years
Categorical

High correlation 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size29.5 KiB
2.0
152 
10.0
122 
1.0
114 
5.0
112 

Length

Max length4
Median length3
Mean length3.244
Min length3

Characters and Unicode

Total characters1622
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10.0
2nd row10.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 152
30.4%
10.0 122
24.4%
1.0 114
22.8%
5.0 112
22.4%

Length

2025-08-19T01:09:15.231296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:09:15.331861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 152
30.4%
10.0 122
24.4%
1.0 114
22.8%
5.0 112
22.4%

Most occurring characters

ValueCountFrequency (%)
0 622
38.3%
. 500
30.8%
1 236
 
14.5%
2 152
 
9.4%
5 112
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1622
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 622
38.3%
. 500
30.8%
1 236
 
14.5%
2 152
 
9.4%
5 112
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1622
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 622
38.3%
. 500
30.8%
1 236
 
14.5%
2 152
 
9.4%
5 112
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1622
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 622
38.3%
. 500
30.8%
1 236
 
14.5%
2 152
 
9.4%
5 112
 
6.9%

customer_rating
Real number (ℝ)

Distinct21
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9972
Minimum3
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-08-19T01:09:15.423980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.1
Q13.5
median4
Q34.5
95-th percentile4.9
Maximum5
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.58178097
Coefficient of variation (CV)0.14554713
Kurtosis-1.1430745
Mean3.9972
Median Absolute Deviation (MAD)0.5
Skewness0.010675027
Sum1998.6
Variance0.3384691
MonotonicityNot monotonic
2025-08-19T01:09:15.532587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4 44
 
8.8%
4.6 33
 
6.6%
3.1 32
 
6.4%
3.8 29
 
5.8%
4.8 26
 
5.2%
4.9 26
 
5.2%
3.4 26
 
5.2%
4.4 25
 
5.0%
4.2 24
 
4.8%
3.2 24
 
4.8%
Other values (11) 211
42.2%
ValueCountFrequency (%)
3 11
 
2.2%
3.1 32
6.4%
3.2 24
4.8%
3.3 24
4.8%
3.4 26
5.2%
3.5 21
4.2%
3.6 17
3.4%
3.7 23
4.6%
3.8 29
5.8%
3.9 23
4.6%
ValueCountFrequency (%)
5 15
3.0%
4.9 26
5.2%
4.8 26
5.2%
4.7 17
3.4%
4.6 33
6.6%
4.5 17
3.4%
4.4 25
5.0%
4.3 19
3.8%
4.2 24
4.8%
4.1 24
4.8%

city
Categorical

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size31.1 KiB
Pune
98 
Hyderabad
71 
Mumbai
70 
Kolkata
69 
Chennai
68 
Other values (2)
124 

Length

Max length9
Median length7
Mean length6.516
Min length4

Characters and Unicode

Total characters3258
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDelhi
2nd rowDelhi
3rd rowBangalore
4th rowHyderabad
5th rowBangalore

Common Values

ValueCountFrequency (%)
Pune 98
19.6%
Hyderabad 71
14.2%
Mumbai 70
14.0%
Kolkata 69
13.8%
Chennai 68
13.6%
Delhi 67
13.4%
Bangalore 57
11.4%

Length

2025-08-19T01:09:15.647927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:09:15.743886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pune 98
19.6%
hyderabad 71
14.2%
mumbai 70
14.0%
kolkata 69
13.8%
chennai 68
13.6%
delhi 67
13.4%
bangalore 57
11.4%

Most occurring characters

ValueCountFrequency (%)
a 532
16.3%
e 361
 
11.1%
n 291
 
8.9%
i 205
 
6.3%
l 193
 
5.9%
u 168
 
5.2%
d 142
 
4.4%
b 141
 
4.3%
h 135
 
4.1%
r 128
 
3.9%
Other values (13) 962
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3258
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 532
16.3%
e 361
 
11.1%
n 291
 
8.9%
i 205
 
6.3%
l 193
 
5.9%
u 168
 
5.2%
d 142
 
4.4%
b 141
 
4.3%
h 135
 
4.1%
r 128
 
3.9%
Other values (13) 962
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3258
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 532
16.3%
e 361
 
11.1%
n 291
 
8.9%
i 205
 
6.3%
l 193
 
5.9%
u 168
 
5.2%
d 142
 
4.4%
b 141
 
4.3%
h 135
 
4.1%
r 128
 
3.9%
Other values (13) 962
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3258
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 532
16.3%
e 361
 
11.1%
n 291
 
8.9%
i 205
 
6.3%
l 193
 
5.9%
u 168
 
5.2%
d 142
 
4.4%
b 141
 
4.3%
h 135
 
4.1%
r 128
 
3.9%
Other values (13) 962
29.5%

platform
Categorical

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size31.6 KiB
Amazon
139 
Croma
122 
Vijay Sales
120 
Flipkart
119 

Length

Max length11
Median length8
Mean length7.432
Min length5

Characters and Unicode

Total characters3716
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAmazon
2nd rowAmazon
3rd rowFlipkart
4th rowVijay Sales
5th rowAmazon

Common Values

ValueCountFrequency (%)
Amazon 139
27.8%
Croma 122
24.4%
Vijay Sales 120
24.0%
Flipkart 119
23.8%

Length

2025-08-19T01:09:15.874711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:09:15.953371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
amazon 139
22.4%
croma 122
19.7%
vijay 120
19.4%
sales 120
19.4%
flipkart 119
19.2%

Most occurring characters

ValueCountFrequency (%)
a 620
16.7%
o 261
 
7.0%
m 261
 
7.0%
r 241
 
6.5%
i 239
 
6.4%
l 239
 
6.4%
A 139
 
3.7%
z 139
 
3.7%
n 139
 
3.7%
C 122
 
3.3%
Other values (11) 1316
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3716
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 620
16.7%
o 261
 
7.0%
m 261
 
7.0%
r 241
 
6.5%
i 239
 
6.4%
l 239
 
6.4%
A 139
 
3.7%
z 139
 
3.7%
n 139
 
3.7%
C 122
 
3.3%
Other values (11) 1316
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3716
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 620
16.7%
o 261
 
7.0%
m 261
 
7.0%
r 241
 
6.5%
i 239
 
6.4%
l 239
 
6.4%
A 139
 
3.7%
z 139
 
3.7%
n 139
 
3.7%
C 122
 
3.3%
Other values (11) 1316
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3716
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 620
16.7%
o 261
 
7.0%
m 261
 
7.0%
r 241
 
6.5%
i 239
 
6.4%
l 239
 
6.4%
A 139
 
3.7%
z 139
 
3.7%
n 139
 
3.7%
C 122
 
3.3%
Other values (11) 1316
35.4%

discount_offered
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.16
Minimum0
Maximum30
Zeros63
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-08-19T01:09:16.032653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median15
Q325
95-th percentile30
Maximum30
Range30
Interquartile range (IQR)20

Descriptive statistics

Standard deviation9.8064639
Coefficient of variation (CV)0.64686437
Kurtosis-1.1937102
Mean15.16
Median Absolute Deviation (MAD)10
Skewness-0.0032769279
Sum7580
Variance96.166733
MonotonicityNot monotonic
2025-08-19T01:09:16.111987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
15 90
18.0%
5 76
15.2%
30 71
14.2%
25 70
14.0%
20 67
13.4%
10 63
12.6%
0 63
12.6%
ValueCountFrequency (%)
0 63
12.6%
5 76
15.2%
10 63
12.6%
15 90
18.0%
20 67
13.4%
25 70
14.0%
30 71
14.2%
ValueCountFrequency (%)
30 71
14.2%
25 70
14.0%
20 67
13.4%
15 90
18.0%
10 63
12.6%
5 76
15.2%
0 63
12.6%

availability
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size32.9 KiB
Out of Stock
265 
In Stock
235 

Length

Max length12
Median length12
Mean length10.12
Min length8

Characters and Unicode

Total characters5060
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOut of Stock
2nd rowOut of Stock
3rd rowIn Stock
4th rowOut of Stock
5th rowIn Stock

Common Values

ValueCountFrequency (%)
Out of Stock 265
53.0%
In Stock 235
47.0%

Length

2025-08-19T01:09:16.223488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:09:16.299527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
stock 500
39.5%
out 265
20.9%
of 265
20.9%
in 235
18.6%

Most occurring characters

ValueCountFrequency (%)
t 765
15.1%
o 765
15.1%
765
15.1%
k 500
9.9%
c 500
9.9%
S 500
9.9%
O 265
 
5.2%
u 265
 
5.2%
f 265
 
5.2%
I 235
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5060
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 765
15.1%
o 765
15.1%
765
15.1%
k 500
9.9%
c 500
9.9%
S 500
9.9%
O 265
 
5.2%
u 265
 
5.2%
f 265
 
5.2%
I 235
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5060
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 765
15.1%
o 765
15.1%
765
15.1%
k 500
9.9%
c 500
9.9%
S 500
9.9%
O 265
 
5.2%
u 265
 
5.2%
f 265
 
5.2%
I 235
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5060
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 765
15.1%
o 765
15.1%
765
15.1%
k 500
9.9%
c 500
9.9%
S 500
9.9%
O 265
 
5.2%
u 265
 
5.2%
f 265
 
5.2%
I 235
 
4.6%

warranty_duration_months
Categorical

High correlation 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
24.0
149 
120.0
125 
12.0
113 
60.0
113 

Length

Max length5
Median length4
Mean length4.25
Min length4

Characters and Unicode

Total characters2125
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row120.0
2nd row120.0
3rd row12.0
4th row24.0
5th row120.0

Common Values

ValueCountFrequency (%)
24.0 149
29.8%
120.0 125
25.0%
12.0 113
22.6%
60.0 113
22.6%

Length

2025-08-19T01:09:16.403248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:09:16.485757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
24.0 149
29.8%
120.0 125
25.0%
12.0 113
22.6%
60.0 113
22.6%

Most occurring characters

ValueCountFrequency (%)
0 738
34.7%
. 500
23.5%
2 387
18.2%
1 238
 
11.2%
4 149
 
7.0%
6 113
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 738
34.7%
. 500
23.5%
2 387
18.2%
1 238
 
11.2%
4 149
 
7.0%
6 113
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 738
34.7%
. 500
23.5%
2 387
18.2%
1 238
 
11.2%
4 149
 
7.0%
6 113
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 738
34.7%
. 500
23.5%
2 387
18.2%
1 238
 
11.2%
4 149
 
7.0%
6 113
 
5.3%

review_sentiment
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size31.7 KiB
Neutral
183 
Negative
163 
Positive
154 

Length

Max length8
Median length8
Mean length7.634
Min length7

Characters and Unicode

Total characters3817
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowPositive
3rd rowPositive
4th rowNeutral
5th rowPositive

Common Values

ValueCountFrequency (%)
Neutral 183
36.6%
Negative 163
32.6%
Positive 154
30.8%

Length

2025-08-19T01:09:16.595477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:09:16.668459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
neutral 183
36.6%
negative 163
32.6%
positive 154
30.8%

Most occurring characters

ValueCountFrequency (%)
e 663
17.4%
t 500
13.1%
i 471
12.3%
a 346
9.1%
N 346
9.1%
v 317
8.3%
u 183
 
4.8%
r 183
 
4.8%
l 183
 
4.8%
g 163
 
4.3%
Other values (3) 462
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3817
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 663
17.4%
t 500
13.1%
i 471
12.3%
a 346
9.1%
N 346
9.1%
v 317
8.3%
u 183
 
4.8%
r 183
 
4.8%
l 183
 
4.8%
g 163
 
4.3%
Other values (3) 462
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3817
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 663
17.4%
t 500
13.1%
i 471
12.3%
a 346
9.1%
N 346
9.1%
v 317
8.3%
u 183
 
4.8%
r 183
 
4.8%
l 183
 
4.8%
g 163
 
4.3%
Other values (3) 462
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3817
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 663
17.4%
t 500
13.1%
i 471
12.3%
a 346
9.1%
N 346
9.1%
v 317
8.3%
u 183
 
4.8%
r 183
 
4.8%
l 183
 
4.8%
g 163
 
4.3%
Other values (3) 462
12.1%

return_status
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size32.9 KiB
Not Returned
268 
Returned
232 

Length

Max length12
Median length12
Mean length10.144
Min length8

Characters and Unicode

Total characters5072
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Returned
2nd rowReturned
3rd rowNot Returned
4th rowNot Returned
5th rowReturned

Common Values

ValueCountFrequency (%)
Not Returned 268
53.6%
Returned 232
46.4%

Length

2025-08-19T01:09:16.770116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:09:16.841109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
returned 500
65.1%
not 268
34.9%

Most occurring characters

ValueCountFrequency (%)
e 1000
19.7%
t 768
15.1%
n 500
9.9%
r 500
9.9%
R 500
9.9%
u 500
9.9%
d 500
9.9%
N 268
 
5.3%
o 268
 
5.3%
268
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5072
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1000
19.7%
t 768
15.1%
n 500
9.9%
r 500
9.9%
R 500
9.9%
u 500
9.9%
d 500
9.9%
N 268
 
5.3%
o 268
 
5.3%
268
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5072
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1000
19.7%
t 768
15.1%
n 500
9.9%
r 500
9.9%
R 500
9.9%
u 500
9.9%
d 500
9.9%
N 268
 
5.3%
o 268
 
5.3%
268
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5072
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1000
19.7%
t 768
15.1%
n 500
9.9%
r 500
9.9%
R 500
9.9%
u 500
9.9%
d 500
9.9%
N 268
 
5.3%
o 268
 
5.3%
268
 
5.3%

complaint_text
Categorical

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size40.1 KiB
Excellent performance.
102 
Slow installation service.
73 
Delivered with damaged packaging.
72 
No issues so far.
68 
Remote stopped working.
66 
Other values (2)
119 

Length

Max length33
Median length25
Mean length24.798
Min length17

Characters and Unicode

Total characters12399
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDelivered with damaged packaging.
2nd rowCooling issue after 2 months.
3rd rowDelivered with damaged packaging.
4th rowNoisy operation at night.
5th rowExcellent performance.

Common Values

ValueCountFrequency (%)
Excellent performance. 102
20.4%
Slow installation service. 73
14.6%
Delivered with damaged packaging. 72
14.4%
No issues so far. 68
13.6%
Remote stopped working. 66
13.2%
Noisy operation at night. 61
12.2%
Cooling issue after 2 months. 58
11.6%

Length

2025-08-19T01:09:16.941571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:09:17.043261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
excellent 102
 
5.9%
performance 102
 
5.9%
slow 73
 
4.3%
installation 73
 
4.3%
service 73
 
4.3%
delivered 72
 
4.2%
with 72
 
4.2%
damaged 72
 
4.2%
packaging 72
 
4.2%
no 68
 
4.0%
Other values (15) 936
54.6%

Most occurring characters

ValueCountFrequency (%)
e 1285
 
10.4%
1215
 
9.8%
o 939
 
7.6%
i 868
 
7.0%
a 784
 
6.3%
t 751
 
6.1%
n 726
 
5.9%
s 719
 
5.8%
r 602
 
4.9%
l 553
 
4.5%
Other values (21) 3957
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12399
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1285
 
10.4%
1215
 
9.8%
o 939
 
7.6%
i 868
 
7.0%
a 784
 
6.3%
t 751
 
6.1%
n 726
 
5.9%
s 719
 
5.8%
r 602
 
4.9%
l 553
 
4.5%
Other values (21) 3957
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12399
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1285
 
10.4%
1215
 
9.8%
o 939
 
7.6%
i 868
 
7.0%
a 784
 
6.3%
t 751
 
6.1%
n 726
 
5.9%
s 719
 
5.8%
r 602
 
4.9%
l 553
 
4.5%
Other values (21) 3957
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12399
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1285
 
10.4%
1215
 
9.8%
o 939
 
7.6%
i 868
 
7.0%
a 784
 
6.3%
t 751
 
6.1%
n 726
 
5.9%
s 719
 
5.8%
r 602
 
4.9%
l 553
 
4.5%
Other values (21) 3957
31.9%

resolved_status
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size32.7 KiB
In Progress
184 
Unresolved
160 
Resolved
156 

Length

Max length11
Median length10
Mean length9.744
Min length8

Characters and Unicode

Total characters4872
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResolved
2nd rowIn Progress
3rd rowUnresolved
4th rowResolved
5th rowResolved

Common Values

ValueCountFrequency (%)
In Progress 184
36.8%
Unresolved 160
32.0%
Resolved 156
31.2%

Length

2025-08-19T01:09:17.193423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:09:17.270654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
in 184
26.9%
progress 184
26.9%
unresolved 160
23.4%
resolved 156
22.8%

Most occurring characters

ValueCountFrequency (%)
e 816
16.7%
s 684
14.0%
r 528
10.8%
o 500
10.3%
n 344
7.1%
v 316
 
6.5%
d 316
 
6.5%
l 316
 
6.5%
I 184
 
3.8%
P 184
 
3.8%
Other values (4) 684
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4872
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 816
16.7%
s 684
14.0%
r 528
10.8%
o 500
10.3%
n 344
7.1%
v 316
 
6.5%
d 316
 
6.5%
l 316
 
6.5%
I 184
 
3.8%
P 184
 
3.8%
Other values (4) 684
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4872
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 816
16.7%
s 684
14.0%
r 528
10.8%
o 500
10.3%
n 344
7.1%
v 316
 
6.5%
d 316
 
6.5%
l 316
 
6.5%
I 184
 
3.8%
P 184
 
3.8%
Other values (4) 684
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4872
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 816
16.7%
s 684
14.0%
r 528
10.8%
o 500
10.3%
n 344
7.1%
v 316
 
6.5%
d 316
 
6.5%
l 316
 
6.5%
I 184
 
3.8%
P 184
 
3.8%
Other values (4) 684
14.0%

review_date
Date

Missing 

Distinct371
Distinct (%)76.5%
Missing15
Missing (%)3.0%
Memory size4.0 KiB
Minimum2022-01-01 00:00:00
Maximum2024-06-18 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-19T01:09:17.394686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:09:17.547511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size31.5 KiB
Hyderabad
102 
Ahmedabad
79 
Chennai
74 
Mumbai
65 
Pune
63 
Other values (2)
117 

Length

Max length9
Median length7
Mean length7.188
Min length4

Characters and Unicode

Total characters3594
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDelhi
2nd rowChennai
3rd rowBangalore
4th rowPune
5th rowPune

Common Values

ValueCountFrequency (%)
Hyderabad 102
20.4%
Ahmedabad 79
15.8%
Chennai 74
14.8%
Mumbai 65
13.0%
Pune 63
12.6%
Delhi 62
12.4%
Bangalore 55
11.0%

Length

2025-08-19T01:09:17.676072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-19T01:09:17.766695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
hyderabad 102
20.4%
ahmedabad 79
15.8%
chennai 74
14.8%
mumbai 65
13.0%
pune 63
12.6%
delhi 62
12.4%
bangalore 55
11.0%

Most occurring characters

ValueCountFrequency (%)
a 611
17.0%
e 435
12.1%
d 362
10.1%
n 266
 
7.4%
b 246
 
6.8%
h 215
 
6.0%
i 201
 
5.6%
r 157
 
4.4%
m 144
 
4.0%
u 128
 
3.6%
Other values (11) 829
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3594
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 611
17.0%
e 435
12.1%
d 362
10.1%
n 266
 
7.4%
b 246
 
6.8%
h 215
 
6.0%
i 201
 
5.6%
r 157
 
4.4%
m 144
 
4.0%
u 128
 
3.6%
Other values (11) 829
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3594
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 611
17.0%
e 435
12.1%
d 362
10.1%
n 266
 
7.4%
b 246
 
6.8%
h 215
 
6.0%
i 201
 
5.6%
r 157
 
4.4%
m 144
 
4.0%
u 128
 
3.6%
Other values (11) 829
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3594
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 611
17.0%
e 435
12.1%
d 362
10.1%
n 266
 
7.4%
b 246
 
6.8%
h 215
 
6.0%
i 201
 
5.6%
r 157
 
4.4%
m 144
 
4.0%
u 128
 
3.6%
Other values (11) 829
23.1%
Distinct485
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size49.1 KiB
2025-08-19T01:09:17.970242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length47
Mean length43.276
Min length34

Characters and Unicode

Total characters21638
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique484 ?
Unique (%)96.8%

Sample

1st rowVoltas Table Top Dishwasher (Model_N299)
2nd rowVoltas Top Load Washing Machine (Model_K244)
3rd rowVoltas Inverter AC Air Conditioner (Model_X75)
4th rowVoltas Window AC Air Conditioner (Model_X101)
5th rowVoltas Split AC Air Conditioner (Model_Y128)
ValueCountFrequency (%)
voltas 476
 
17.6%
air 148
 
5.5%
top 122
 
4.5%
refrigerator 100
 
3.7%
water 83
 
3.1%
loading 81
 
3.0%
dispenser 81
 
3.0%
cooler 78
 
2.9%
dishwasher 77
 
2.8%
machine 75
 
2.8%
Other values (553) 1383
51.1%
2025-08-19T01:09:18.658145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2204
 
10.2%
o 2065
 
9.5%
e 1652
 
7.6%
l 1332
 
6.2%
a 1157
 
5.3%
r 1108
 
5.1%
i 1001
 
4.6%
s 970
 
4.5%
t 960
 
4.4%
d 818
 
3.8%
Other values (53) 8371
38.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21638
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2204
 
10.2%
o 2065
 
9.5%
e 1652
 
7.6%
l 1332
 
6.2%
a 1157
 
5.3%
r 1108
 
5.1%
i 1001
 
4.6%
s 970
 
4.5%
t 960
 
4.4%
d 818
 
3.8%
Other values (53) 8371
38.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21638
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2204
 
10.2%
o 2065
 
9.5%
e 1652
 
7.6%
l 1332
 
6.2%
a 1157
 
5.3%
r 1108
 
5.1%
i 1001
 
4.6%
s 970
 
4.5%
t 960
 
4.4%
d 818
 
3.8%
Other values (53) 8371
38.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21638
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2204
 
10.2%
o 2065
 
9.5%
e 1652
 
7.6%
l 1332
 
6.2%
a 1157
 
5.3%
r 1108
 
5.1%
i 1001
 
4.6%
s 970
 
4.5%
t 960
 
4.4%
d 818
 
3.8%
Other values (53) 8371
38.7%
Distinct497
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size33.2 KiB
2025-08-19T01:09:18.955793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length13.5
Mean length10.726
Min length5

Characters and Unicode

Total characters5363
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique494 ?
Unique (%)98.8%

Sample

1st rowarjun_20
2nd rowsanjay219
3rd rownisha_buyer0
4th rowpreeti_deals8
5th rowswati.buyer01
ValueCountFrequency (%)
ajay.3 2
 
0.4%
rohit_9 2
 
0.4%
amit.6 2
 
0.4%
sanjay219 1
 
0.2%
ajaydeals2 1
 
0.2%
sanjay.zone8 1
 
0.2%
komal.450 1
 
0.2%
preeti.buyer7 1
 
0.2%
vinod.4 1
 
0.2%
divya_user920 1
 
0.2%
Other values (487) 487
97.4%
2025-08-19T01:09:19.429085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 581
 
10.8%
e 497
 
9.3%
i 388
 
7.2%
s 342
 
6.4%
r 325
 
6.1%
n 285
 
5.3%
h 197
 
3.7%
. 171
 
3.2%
o 169
 
3.2%
_ 153
 
2.9%
Other values (24) 2255
42.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5363
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 581
 
10.8%
e 497
 
9.3%
i 388
 
7.2%
s 342
 
6.4%
r 325
 
6.1%
n 285
 
5.3%
h 197
 
3.7%
. 171
 
3.2%
o 169
 
3.2%
_ 153
 
2.9%
Other values (24) 2255
42.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5363
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 581
 
10.8%
e 497
 
9.3%
i 388
 
7.2%
s 342
 
6.4%
r 325
 
6.1%
n 285
 
5.3%
h 197
 
3.7%
. 171
 
3.2%
o 169
 
3.2%
_ 153
 
2.9%
Other values (24) 2255
42.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5363
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 581
 
10.8%
e 497
 
9.3%
i 388
 
7.2%
s 342
 
6.4%
r 325
 
6.1%
n 285
 
5.3%
h 197
 
3.7%
. 171
 
3.2%
o 169
 
3.2%
_ 153
 
2.9%
Other values (24) 2255
42.0%

Interactions

2025-08-19T01:09:09.855963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:09:08.075401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:09:08.734947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:09:09.379590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:09:09.953336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:09:08.223126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:09:08.938886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:09:09.495408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:09:10.058932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:09:08.386897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:09:09.118575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:09:09.606698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:09:10.187020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:09:08.558994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:09:09.270922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-19T01:09:09.720433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-19T01:09:19.620799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
availabilitycapacity_kgcapacity_literscapacity_place_settingscapacity_tonscitycolorcomplaint_textcustomer_ratingdiscount_offeredenergy_rating_starsfeature_1platformprice_inrproduct_categoryresolved_statusreturn_statusreview_sentimentreviewer_locationsub_typetechnologywarranty_duration_monthswarranty_years
availability1.0000.0000.0000.0000.0000.0930.0000.0000.0000.0000.0000.0470.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
capacity_kg0.0001.0000.0000.0000.0000.0690.0650.0000.0420.0000.0000.3130.0290.0470.3980.0000.0480.0600.0750.3810.3850.0790.070
capacity_liters0.0000.0001.0000.0410.0080.0510.0000.000-0.023-0.0480.0000.4390.0000.0130.4740.0310.0240.0570.0650.4470.4480.0830.078
capacity_place_settings0.0000.0000.0411.0000.0000.0000.0000.0000.0000.0460.0000.3930.0250.0000.4550.0000.0000.0070.0240.4390.4420.0000.000
capacity_tons0.0000.0000.0080.0001.0000.0140.0560.0000.0000.0000.0000.4140.0000.0000.4290.0260.0000.0000.0000.4300.4270.0000.022
city0.0930.0690.0510.0000.0141.0000.0900.0400.0550.0470.0850.0000.0000.0420.0000.0620.0550.0000.0370.0000.0210.0000.024
color0.0000.0650.0000.0000.0560.0901.0000.0600.0000.0870.0000.0000.0000.0000.0000.0120.0000.0000.0420.0520.0000.0000.030
complaint_text0.0000.0000.0000.0000.0000.0400.0601.0000.0000.0510.0000.0480.0800.0570.0000.0720.0000.0000.0480.0000.0000.0000.000
customer_rating0.0000.042-0.0230.0000.0000.0550.0000.0001.0000.0270.0610.0000.078-0.0050.0000.0000.0000.0000.0460.0000.0000.0000.000
discount_offered0.0000.000-0.0480.0460.0000.0470.0870.0510.0271.0000.0820.0000.0480.0130.0000.0470.1120.0000.0590.0000.0000.0000.000
energy_rating_stars0.0000.0000.0000.0000.0000.0850.0000.0000.0610.0821.0000.0000.0000.0000.0000.0770.0640.0000.0000.0000.0610.0000.033
feature_10.0470.3130.4390.3930.4140.0000.0000.0480.0000.0000.0001.0000.0000.0870.9090.0970.0000.0000.0650.5420.7070.0000.000
platform0.0470.0290.0000.0250.0000.0000.0000.0800.0780.0480.0000.0001.0000.0690.0000.1060.0000.0000.0000.0000.0810.0000.000
price_inr0.0000.0470.0130.0000.0000.0420.0000.057-0.0050.0130.0000.0870.0691.0000.0330.0870.0660.0000.0000.0490.0450.0000.000
product_category0.0000.3980.4740.4550.4290.0000.0000.0000.0000.0000.0000.9090.0000.0331.0000.1130.0000.0480.0000.9420.9420.0610.061
resolved_status0.0000.0000.0310.0000.0260.0620.0120.0720.0000.0470.0770.0970.1060.0870.1131.0000.0000.0000.0000.0960.1030.0600.070
return_status0.0000.0480.0240.0000.0000.0550.0000.0000.0000.1120.0640.0000.0000.0660.0000.0001.0000.0200.0690.0000.0000.0000.000
review_sentiment0.0000.0600.0570.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0480.0000.0201.0000.0000.0000.0650.0830.079
reviewer_location0.0000.0750.0650.0240.0000.0370.0420.0480.0460.0590.0000.0650.0000.0000.0000.0000.0690.0001.0000.1090.0000.0000.000
sub_type0.0000.3810.4470.4390.4300.0000.0520.0000.0000.0000.0000.5420.0000.0490.9420.0960.0000.0000.1091.0000.8130.0290.051
technology0.0000.3850.4480.4420.4270.0210.0000.0000.0000.0000.0610.7070.0810.0450.9420.1030.0000.0650.0000.8131.0000.0670.066
warranty_duration_months0.0000.0790.0830.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0610.0600.0000.0830.0000.0290.0671.0000.937
warranty_years0.0000.0700.0780.0000.0220.0240.0300.0000.0000.0000.0330.0000.0000.0000.0610.0700.0000.0790.0000.0510.0660.9371.000

Missing values

2025-08-19T01:09:10.400725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-19T01:09:10.660553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-08-19T01:09:10.862008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

product_idproduct_categorysub_typemodel_namecapacity_tonscapacity_literscapacity_kgcapacity_place_settingstechnologyfeature_1energy_rating_starscolorprice_inrmanufacturing_datewarranty_yearscustomer_ratingcityplatformdiscount_offeredavailabilitywarranty_duration_monthsreview_sentimentreturn_statuscomplaint_textresolved_statusreview_datereviewer_locationproduct_nameusername
0PID433097DishwasherTable TopModel_N2991.550.07.012.0Electronic ControlProSmart Inverter Motor3.0Blue39733.02023-03-0210.03.9DelhiAmazon5.0Out of Stock120.0PositiveNot ReturnedDelivered with damaged packaging.Resolved2023-08-28DelhiVoltas Table Top Dishwasher (Model_N299)arjun_20
1PID251899Washing MachineTop LoadModel_K2441.550.06.512.0Fully-AutomaticProSmart Inverter Motor3.0Silver56336.02022-07-2110.04.2DelhiAmazon25.0Out of Stock120.0PositiveReturnedCooling issue after 2 months.In Progress2024-01-14ChennaiVoltas Top Load Washing Machine (Model_K244)sanjay219
2PID857398Air ConditionerInverter ACModel_X752.050.07.012.0InverterTurbo Mode1.0Blue52924.02022-02-241.04.8BangaloreFlipkart20.0In Stock12.0PositiveNot ReturnedDelivered with damaged packaging.Unresolved2023-07-20BangaloreVoltas Inverter AC Air Conditioner (Model_X75)nisha_buyer0
3PID934528Air ConditionerWindow ACModel_X1011.050.07.012.0Non-InverterSelf Diagnosis3.0Grey19256.02024-05-302.04.4HyderabadVijay Sales10.0Out of Stock24.0NeutralNot ReturnedNoisy operation at night.Resolved2023-01-13PuneVoltas Window AC Air Conditioner (Model_X101)preeti_deals8
4PID952837Air ConditionerSplit ACModel_Y1281.050.07.012.0Non-InverterTurbo Mode3.0Black44924.02024-03-172.03.3BangaloreAmazon5.0In Stock120.0PositiveReturnedExcellent performance.Resolved2023-10-18PuneVoltas Split AC Air Conditioner (Model_Y128)swati.buyer01
5PID552702RefrigeratorDouble DoorModel_U3321.5450.07.012.0Direct CoolActive Fresh Blue Light1.0Grey29024.02022-08-0110.03.1KolkataFlipkart30.0In Stock120.0NeutralNot ReturnedExcellent performance.Resolved2023-10-25ChennaiVoltas Double Door Refrigerator (Model_U332)sanjay_reviews74
6PID509633Water DispenserBottom LoadingModel_W1261.515.07.012.0Compressor CoolingHot, Normal & Cold5.0White80497.0NaT2.04.1PuneCroma25.0In Stock24.0NegativeNot ReturnedDelivered with damaged packaging.Resolved2022-07-20DelhiVoltas Bottom Loading Water Dispenser M(odel_W126)ajay_zone42
7PID430190Washing MachineTop LoadModel_B4951.550.06.512.0Semi-AutomaticGentleWave Drum3.0Grey13928.02023-06-2010.04.5BangaloreVijay Sales20.0Out of Stock120.0NegativeReturnedRemote stopped working.Resolved2022-08-21PuneVoltas Top LoadW ashing Machine (Model_B495)vinod9
8PID482549RefrigeratorSplit ACModel_K1661.050.07.012.0Non-InverterTurbo Mode4.0Silver18262.02023-12-055.03.4BangaloreAmazon5.0Out of Stock60.0NeutralReturnedNo issues so far.Unresolved2022-11-09MumbaiVlotas Single Door Refrigerator (Model_N325)radha_151
9PID164915Water DispenserBottom LoadingModel_Y4401.520.07.012.0Compressor CoolingHot & Cold5.0Black47034.02023-08-255.04.9BangaloreFlipkart20.0Out of Stock60.0NeutralNot ReturnedSlow installation service.In Progress2024-06-01DelhiVoltas Bottom Loading Water Dispenser (Model_Y440)radhaindia70
product_idproduct_categorysub_typemodel_namecapacity_tonscapacity_literscapacity_kgcapacity_place_settingstechnologyfeature_1energy_rating_starscolorprice_inrmanufacturing_datewarranty_yearscustomer_ratingcityplatformdiscount_offeredavailabilitywarranty_duration_monthsreview_sentimentreturn_statuscomplaint_textresolved_statusreview_datereviewer_locationproduct_nameusername
490PID445336Air CoolerDesertModel_L1931.550.07.012.0Evaporative CoolingRemote Control3.0Grey18654.02023-05-131.04.9HyderabadAmazon15.0Out of Stock12.0NeutralReturnedDelivered with damaged packaging.In Progress2023-10-12PuneVoltas Desert Air Cooler (Model_L193)suresh.91
491PID108644Air ConditionerInverter ACModel_C21.050.07.012.0InverterTurbo Mode4.0Silver55135.02023-08-045.05.0HyderabadAmazon25.0Out of Stock60.0PositiveNot ReturnedDelivered with damaged packaging.ResolvedNaTMumbaiVoltas Inverter AC Air Conditioner (Model_C2)ajay7
492PID878002RefrigeratorBottom LoadingModel_F3171.5250.07.012.0Direct CoolStoreFresh+1.0Black61806.02022-01-311.04.0PuneFlipkart0.0Out of Stock12.0NeutralReturnedRemote stopped working.In Progress2022-07-09HyderabadVoltas Single Door Refrigerator (Model_F317)deepa.zone5
493PID275253RefrigeratorSingle DoorModel_Z1291.550.07.012.0Frost FreeStoreFresh+5.0Black56407.02024-06-162.03.1KolkataCroma15.0Out of Stock24.0NeutralNot ReturnedRemote stopped working.Resolved2022-03-28HyderabadVoltas Single Door Refrigerator (Model_Z129)vikasexpress2
494PID322665Air ConditionerSplit ACModel_B1311.050.07.012.0Compressor CoolingAdjustable Cooling3.0Grey67872.02023-05-0710.04.1HyderabadCroma10.0Out of Stock120.0NegativeReturnedNo issues so far.Unresolved2022-03-01AhmedabadVoltas Split AC Air Conditioner (Model_B131)deepa85
495PID408384Air CoolerDesertModel_R3031.590.07.012.0Evaporative CoolingIce Chamber5.0White46386.02022-03-0310.03.2ChennaiFlipkart10.0Out of Stock120.0PositiveReturnedNoisy operation at night.In Progress2023-03-11MumbaiVoltas Desert Air Cooler (Model_R303)seema.buyer22
496PID319590Washing MachineTop LoadModel_N2211.550.06.012.0Semi-AutomaticProSmart Inverter Motor2.0Blue19647.02022-06-232.03.2ChennaiAmazon0.0Out of Stock24.0NegativeReturnedExcellent performance.In Progress2023-11-09HyderabadVoltas Top Load Washing Machine (Model_N221)shilpa767
497PID390184DishwasherFull SizeModel_A01.550.07.012.0Electronic ControlHygiene+5.0Grey14754.02022-08-2810.04.5MumbaiVijay Sales20.0Out of Stock120.0PositiveNot ReturnedCooling issue after 2 months.In Progress2022-06-30HyderabadVoltas Full Size Dishwasher (Model_W230)rohitzone84
498PID966079Washing MachineFront LoadModel_S961.550.06.012.0Fully-AutomaticProSmart Inverter Motor3.0White58325.02023-01-191.04.8PuneCroma30.0In Stock12.0NegativeReturnedExcellent performance.Resolved2022-06-21HyderabadVoltas Front Load Washing Machine (Model_S96)nisha_52
499PID937912DishwasherFull SizeModel_T1751.550.07.012.0Electronic ControlProSmart Inverter Motor4.0Black71284.02023-12-1310.04.2PuneCroma0.0In Stock120.0NegativeReturnedSlow installation service.Resolved2024-01-12AhmedabadVoltas Full Size Dishwasher (Model_T175)komal.shop392